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Abstract

Background

The most prominent non-occupational source of exposure to methylmercury is the consumption
of fish. In this study we examine a fish consuming population to determine the extent
of temporal exposure and investigate the extent to which single time estimates of
methylmercury exposure based on blood-Hg concentration can provide reliable estimates
of longer-term average exposure.

Methods

Blood-mercury levels were obtained from a portion of the Arsenic Mercury Intake Biometric
Study (AMIBS) cohort. Specifically, 56 Japanese women residing in the Puget Sound
area of Washington State, US were sampled on three occasions across a one-year period.

Results

An average of 135 days separated samples, with mean blood-mercury levels for the visits
being 5.1, 6.6 and 5.0 μg/l and geometric means being 2.7, 4.5 and 3.1 μg/l. The blood-mercury
levels in this group exceed national averages with geometric means for two of the
visits being between the 90th and 95th percentiles of nationally observed levels and the lowest geometric mean being between
the 75th and 90th percentile. Group means were not significantly different across sampling periods
suggesting that exposure of combined subjects remained relatively constant. Comparing
intra-individual results over time did not reveal a strong correlation among visits
(r = 0.19, 0.50, 0.63 between 1st and 2nd, 2nd and 3rd, and 1st and 3rd sample results, respectively). In comparing blood-mercury levels across two sampling
interval combinations (1st and 2nd, 2nd and 3rd, and 1st and 3rd visits, respectively), 58% (n = 34), 53% (n = 31) and 29% (n = 17) of the individuals
had at least a 100% difference in blood-Hg levels.

Conclusions

Point estimates of blood-mercury, when compared with three sample averages, may not
reflect temporal variability and individual exposures estimated on the basis of single
blood samples should be treated with caution as indicators of long-term exposure.
Reliance on single blood samples can make predicting ongoing methylmercury exposure
highly speculative due to the large intra-individual variability.

Keywords:

Background

By far, the most important non-occupational pathway for methylmercury (MeHg) exposure
is fish consumption [1]. None of the forms of mercury have a role in normal human physiological function.
Children who have been exposed in utero to MeHg can develop neuropsychological deficits [1]. To better understand the exposure-effect relationship for MeHg within individuals
or populations, indicators of exposure, such as mercury (Hg) concentrations in hair,
toenails, maternal-blood, umbilical cord tissue and cord-blood, have been used.

These biological indicators have also been used to measure temporal variability in
exposure. Repeated sampling of individuals over time offers the hope of capturing
intra-individual variability including variations in consumption behavior resulting
from seasonal differences in species availability, and episodic consumption of fish
with high MeHg concentrations. Blood-Hg data for the general US population from 1999–2004
suggest that levels may be decreasing with time though the reasons for this decrease
are unclear [2]. In an indigenous Canadian population followed across five years, hair-Hg levels
were shown to have seasonal variation with the highest mean hair-Hg concentrations
approaching 17 mg/kg and lowest mean values below 5 mg/kg within a one year cycle
[3]. There have also been studies in which hair-Hg levels were examined across gestation
or across a one year period within fish consuming populations [4-7]. Associations between Hg in blood and hair have also been examined within the same
population at ages 7 and 14 [8]. Although these data have provided some insight into temporal variation in Hg body
burden levels, multiple time estimates of MeHg exposure within a population using
blood-Hg levels has not been examined. Further, most MeHg exposure data based on biological
indicators have been derived from biological samples collected at only a single time
point. As part of the Arsenic Mercury Intake Biometric Study (AMIBS), we examined
longitudinal data for blood-Hg levels within the population of Japanese women living
in the Puget Sound area of Washington State (US) at three time points. MeHg exposure
is of importance as this population consumes substantially more fish than the national
average [9,10]. The main goals of this work were two-fold: to examine temporal blood-Hg levels within
this group and to quantify intra-individual blood-Hg concentration variability across
a period of approximately one year. This information will allow for a better understanding
of potential misclassification of exposure resulting from intra-individual variability
in temporal blood-Hg levels.

Methods

AMIBS

Of the 106 Japanese women participants in AMIBS, this manuscript describes the results
from 56 Japanese women who provided blood samples during all three clinic visits.
In addition to the Japanese participants, AMIBS includes 108 Korean women participants
but only the Japanese participant data are discussed as only they provided blood samples
across all three visits.

Detailed descriptions of AMIBS have been published [9,10]. The study included women of childbearing age (18 – 45 years of age), who identified
themselves as Korean, Japanese or, of Japanese or Korean descent, and who had lived
in the Puget Sound area of Washington State, US, for at least 6 months. To participate,
women had to be willing to provide a hair sample from the nape of the neck (0.5 cm
in diameter) and participate in a fish consumption survey (FCS). Enrollees also completed
a self-administered food frequency questionnaire (FFQ) and could provide additional
biological samples (urine, blood and/or toenails) at their discretion.

The Japanese participants were interviewed three times across 14 months. On the 1st, 2nd and 3rd visits, data were obtained for 106, 90, and 85 individuals, respectively. Participants
returning for 2nd and 3rd visits were interviewed in the same manner as during the 1st visit except the recall time period for the FFQ and FCS was different between 1st and 2nd/3rd visits. This has previously been described but in brief, for the 1st visit, the surveys were open-ended and individuals could respond to questions about
their fish consumption behavior spanning a year-long period while for the 2nd and 3rd visits, information obtained from the surveys covered the last two weeks prior to
the date of the visit; thus they were two-week recall surveys [7]. Individual estimates of fish and Hg intake have been described previously and these
data were used in this study for comparative purposes with blood-Hg levels [9,10]. Briefly, a detailed FCS with pictorial representations of approximately 70 fish
species with multiple names in three languages along with fish portion models for
cooked and raw fish were used to derive species-specific intake data for each individual
which when combined provided for an estimated total daily fish ingestion rate. Species
specific-Hg levels were obtained from the literature and from lab analyses conducted
to determine Hg concentrations from fish species (20 species analyzed) for which there
were no or limited available data [9]. Specifically, fish commonly consumed by the Japanese and Korean populations in the
Puget Sound area were purchased at local Asian markets and analyzed for total Hg.
Estimated species specific fish intake and Hg fish tissue concentrations allowed for
an estimated Hg ingestion rate to be determined for each individual. Informed consent
was obtained from all study participants, and the study design and materials were
approved by the State of Washington Department of Social and Health Services Human
Research Review Board.

Blood sampling and hg analysis

Preparation and analysis were carried out following method EPA SW 846 7471B [11]. Blood samples were drawn at the Nadeshiko Clinic at the time of the interview. EDTA
evacuated tubes were used to obtain 15 mls of blood from each individual. Samples
were placed on ice, and transported to a certified laboratory (AMTEST Inc., Redmond,
WA) where they were stored at −10 C until analyses were conducted to determine levels
for total Hg. Whole blood samples, blanks and certified reference materials were analyzed
using cold-vapor atomic-absorption spectrometry, with a detection limit of 0.2 μg/l.
Batch sample analyses were conducted reflecting the clinic visit periods and samples
were run in duplicate and matrix spikes were run at a rate of 10%. The lack of precision
for the method (coefficient of variation from repeated measurement of reference materials)
was 5.8%, 7.6% and 5.4% at blood-Hg levels of 1.1, 15.7 and 31.7 μg/l, respectively.

Statistical analyses

A repeated-measures one-way ANOVA was used to compare blood-Hg levels among sample
intervals. Mean individual blood-Hg levels based on pregnancy status were determined
and then compared using Student’s t-test. Also, mean individual blood-Hg levels adjusted for change in plasma volume
during pregnancy were compared with mean levels from non-pregnant participants using
Student’s t-test [5,12]. In addition, minimum and maximum blood Hg concentrations were identified for each
individual and Student’s t-test was used comparing first the minimum and then the maximum Hg concentrations
grouped by pregnancy status. Intra-individual variability in blood-Hg was investigated
by examining observed absolute change across clinic visits and by testing if variability
increased as the maximum blood-Hg levels increased. Pearson product–moment correlation
coefficients were determined to compare blood-Hg levels with estimated intake values
of fish and with estimated intake of Hg. In addition, these correlations were obtained
with and without adjusting for actual individual body weights and pregnancy status.
Statistical analyses were performed using a significance level of 5% (p < 0.05) using
Stata (Stata Corporation, College Station, Texas), Excel (Microsoft Corporation, Redmond,
Washington) and SPSS (SPSS Inc., Chicago, Illinois).

Results

The results of the blood-Hg concentrations for the participants who completed three
clinic visits (n = 56) are provided in Table 1. No significant difference in blood-Hg concentrations between pregnant (n = 22) and
non-pregnant (n = 34) participants was observed (p < 0.05) when comparing means. Because
the stage of pregnancy may affect maternal blood-Hg levels, the minimum and maximum
blood-Hg levels were identified for each individual and compared by pregnancy status.
Again, no significant difference was observed for either the lowest or the highest
concentrations. In another analysis examining the influence of pregnancy on blood-Hg
levels, blood-Hg levels were adjusted for blood volume based on time of gestation
with no significant difference observed (p < 0.05) between pregnant and non-pregnant
participants. The average number of days between visits was 135 (± 19) days with the
shortest interval being 100 days and the longest, 229 days. The interquartile range
was 125 to 143 days. The mean time period spanned by the three visits was 270 days.
The blood-Hg levels of these Japanese fish consumers were compared with the 2003–2006
National Health and Nutrition Examination Survey (NHANES) blood-Hg data which reflect
the US as a whole (Table 1) [13]. Mean values for the Japanese participants greatly exceed the national average. Geometric
mean and median blood-Hg values for the 2nd and 3rd visits of the Japanese participants were between the 90th and 95th percentiles of the levels observed in NHANES. The 1st visit geometric mean and median values fell between the 75th and 90th percentile of the national distribution. The arithmetic mean values for each of the
three visits were close to the 5.8 μg/l value associated with a MeHg uptake equivalent
to the RfD [14].

Table 1.Blood-Hg level (μg/l) comparison between Japanese from three clinic visits with general
US population data

Distributional data of blood-Hg levels for all clinic visits are presented as box-plots
in Figure 1. There was no significant difference (p <0.05) in blood-Hg levels observed among
the three sampling visits. However, the relatively small sample limited the power
to detect differences in the blood-Hg levels at the three sampling visits. The means
for each sample period (5.1, 6.6 and 5.0 μg/l) were not significantly different. Respective
95th percentile confidence intervals were 2.25, 1.49 and 1.35, and coefficients of variation
were 1.7, 0.87 and 1.0. Though means of the distributions of these participants did
not change significantly across sampling periods, many individual blood-Hg levels
varied by 100% or more. The absolute change in blood-Hg levels on an individual basis
between successive samples across the three visits is depicted in Figure 2.

Figure 1.Blood-Hg levels for each visit (n = 56).a Blood-Hg values in μg/l. Medians are middle lines within box. Top and bottom of box
represents upper and lower quartile values, respectively. Upper and lower whiskers
represent sample maximum and minimum values, respectively. Values marked with “°”
and “*” are outliers as defined by being 1.5x and 3x the interquartile range, respectively.
Four (25.1, 29.3, 35.0 and 44.3), three (23.7, 24.3 and 28.2) and one (33.9) blood-Hg
values are not depicted for the 1st, 2nd and 3rd visits, respectively, as they are off the scale provided. Distribution mean values
do not differ significantly (p <0.05). Horizontal line represents 5.8 μg/l blood-Hg
(the RfD equivalent).

Evidence of a strong monotonic agreement among visits was not discernable as the number
of individuals with increasing blood-Hg levels between the 1st and 3rd visit was 35 (63%) and between 1st and 2nd and 2nd and 3rd was 42 (75%) and 24 (43%),
respectively. Correlations between all three groups were weak to moderate (r = 0.19,
0.50, 0.63 between 1st and 2nd, 2nd and 3rd, and 1st and 3rd sample results, respectively; p < 0.05 for all). These modest correlations and the
lack of a strong agreement were reflected in the variability observed in blood-Hg
levels in individuals across the three successive clinic visits during the study period
(Figure 2). For 58% (n = 34), 53% (n = 31) and 29% (n = 17) of the individuals there was a
minimum 100% change in blood-Hg levels between visits (1st and 2nd, 2nd and 3rd, and 1st and 3rd visits, respectively). In total, there were 82 blood-Hg measurements taken at two
different time points that were separated by at least a two-fold difference and of
these, 27 (33%) decreased between sampling time points while 55 (67%) increased. Of
the 10, 21 and 11 individuals that exceeded 5.8 μg/l of blood-Hg on the 1st, 2nd and 3rd visits, respectively, only one individual remained consistently above this RfD equivalent
for exposure during the study period. All other individuals that exceeded 5.8 μg/l
had at least one sample value that was below the 5.8 μg/l level. Percent differences
from the individual mean values in blood-Hg concentrations for each individual, as
determined by dividing each sample concentration by the three-sample average (from
the three visits), ranged from −95% to +179%. The single sample estimates had an average
observed difference of 44% when compared with the three-sample average and only 61%
of the samples (103 of 168) were within 50% of the three-sample average.

Intra-individual variability was examined as a function of blood-Hg concentration
to test if variability increased as the maximum blood-Hg levels increased. Individual
maximum blood-Hg concentration was plotted against the difference between the individual’s
maximum and minimum blood-Hg concentrations as a percentage of the maximum blood-Hg
concentration. Variability was linear across the blood-Hg concentrations observed
in this study (r = 0.42) although sensitivity analyses indicated that those individuals
with the highest maximum (and mean) blood-Hg levels (mean >10 μg/l; n = 6; Figure 2) had greater intra-individual variability. Variability decreased when omitting these
individuals resulting with markedly improved correlation of r = 0.64 (data not shown).

Blood-Hg levels were compared with estimated intake values of fish and Hg with and
without controlling for individual body weight as well as pregnancy status across
the clinic visits. An example of correlation is provided in Figure 3 which depicts data from the 2nd and 3rd visits combined for all participants such that each individual provides two data
points; for each visit, individual blood-Hg values are plotted against FCS derived
data that estimated individual Hg intake on a per kilogram body weight basis. Correlation
was based on individual mean values of the two measurements and was not strong (r = 0.25,
r2 = 0.06, p < 0.05). For all analyses conducted across the given time periods, correlation
coefficients did not exceed 0.3 and no r2 value exceeded 0.1 (p < 0.05).

Figure 3.Example of correlation between estimated Hg intake based on food consumption survey
and blood-Hg levels. Example is of data from the 2nd and 3rd visits (r = 0.25). For all correlation analyses conducted between Hg intake and blood
levels across the study period, no correlation coefficient exceeded 0.3 and regression
models did not fit the data (all r2 < 0.1).

Discussion

To better define dose–response associations linking MeHg exposure with deleterious
effects, whether the effect be neurodevelopmental or cardiovascular, it is important
to reduce uncertainty and address the limitations of exposure assessments. The relationship
between dose and response may be obscured when biomarker values are unable to adequately
reflect ongoing exposure. Single time point measurements are subject to a range of
factors (e.g. variations in consumption frequency, magnitude and duration) that can
lead to uncertainty when using single samples to estimate average MeHg exposure over
time [15]. In addition, since biological samples integrate MeHg exposure over variable periods
of time, the use of such measures can result in uncertainty when attempting to estimate
exposure over a specific period of time (e.g., a single trimester of gestation) [1]. These considerations become particularly important when MeHg biomarkers are employed
to reconstruct the intake dose associated with an observed biomarker-outcome relationship
such as those observed in the Faroe Island study. Such reconstructions are required
in order to convert such data into fish consumption advisories [16]. This work suggests that, on an individual basis, blood-Hg levels from separate exposure
time periods do not provide a reliable estimate of longer term exposure as represented
by a three sample average.

Single time point blood-Hg analysis reflects the aggregate of recent as well as less
recent exposure periods. Blood samples were obtained so that the measured Hg levels
represented exposure periods largely independent of each other with respect to the
half-life of MeHg in blood. The half-life of MeHg in blood is considered to be approximately
50 days and more than two half-lives separated each participant’s samples in this
study [17,18]. Accordingly, less than 25% of an individual’s prior mercury exposure will be reflected
in the blood-Hg levels observed between successive samples. This tended to minimize
(but not eliminate) auto-correlation among the samples. The similarity in the pooled
distribution of blood-Hg values between sampling events, based on group mean values
that did not differ over time, is consistent with previous observation made by Tsuchiya
et al. [7] in hair samples. As the individual sample values were not constant over time, the
sum of intra-individual variabilities across the sampling periods appear to regress
to the sample mean.

In contrast to the temporal stability of the three blood-Hg levels for the group as
a whole, there was marked change over time in individual blood-Hg levels. Blood data
show that separate and nearly independent samples obtained within a time period of
several months can fluctuate by more than 100% and individual sample values exceed
the individual’s average across the specified time period by greater than 50%. Further,
the proportional variability in blood-Hg was similar for individuals at all levels
of blood-Hg with the exception of those above the 90th percentile in blood-Hg. These data may reflect both intra-individual pharmacokinetic
variability as well as the change in MeHg intake observed across sample intervals.
Accordingly, the temporal blood-Hg variability may reflect short-term changes in MeHg
intake, absorption, distribution, metabolism, and elimination.

The temporal changes in blood-Hg levels may aid in explaining the large observed disparity
in hair to blood ratios (mg Hg/kg hair: μg Hg/ml blood) found in the literature that
range from less than 100:1 to approximately 400:1 [1]. A ratio of 250:1 is generally accepted for individuals having reasonably constant
exposure levels. Yet, within this group of Japanese women, the ratios of the proximal
6 cm hair strand mean Hg levels (described previously, [7]) to the whole blood mean Hg levels across the three visits were 373:1, 242:1 and
300:1. The overall three-sample mean was 305:1.

There are two shortcomings with this work that need be noted. First, comparisons between
mean blood-Hg levels, mean blood-Hg levels adjusted for change in blood volume during
gestation, as well as minimum and maximum levels led to no observable difference by
pregnancy status. Although no data exist from other sources that permit a comparison
of these two groups, pregnant women have a decrease in Hg body burden between the
2nd and 3rd trimester possibly due to increased blood volume or transfer of Hg across the placental
barrier to the fetus [5,12]. Our study was not developed to address this issue as participants were accepted
into the study at various time periods during gestation with several women becoming
pregnant during the study period resulting in only a few women being enrolled for
the last two trimesters of pregnancy. Nonetheless, it seems reasonable that any pregnancy-related
difference in blood Hg concentration was obscured by the overall intra-individual
temporal variability. Further, the small sample does not provide for sufficient power
to observe a difference between groups or to see a significant decrease in body burden
across the last two trimesters of pregnancy. Second, since samples were analyzed for
total blood-Hg, the results do not delineate between organic and inorganic forms of
Hg. We cannot exclude exposure through inhalation (Hg vapor from dental amalgams),
ingestion (drinking-water and food), or even from medical treatments, as well as possible
contribution from indoor air [1,19,20]. Also, there is the ritual use of Hg in folk medicine within several populations
that may allow for exposure to elemental or inorganic Hg salts [20].

These additional exposures could have impacted blood-Hg levels and affected correlations
between fish consumption and blood-Hg levels. Comparisons between blood-Hg and the
FCS reflecting dietary practices across the preceding 14 days provided for low correlations.
Although the FCS was a robust instrument with pictorials and species names in multiple
languages and used fish models for determining portion size, the estimated intake
data did not provide for a precise representation of actual Hg intake [9. 10]. Not
all fish species consumed by this population were analyzed for Hg. For the species
not analyzed directly, literature based estimates of species specific Hg levels were
used. These values may not have accurately reflected the same species or may not have
reflected regional differences among the same species. Other factors that could have
impacted the relationship between estimated Hg intake and body burden levels include
recall bias, variability in toxicokinetics, nutritional status and dietary interactions
[1,21-23].

The results of this work brings into question the use of blood-Hg samples collected
at a single point in time to predict average exposure and risk. Physicians have been
advised to obtain blood-Hg levels in individuals considered to be exposed to MeHg
at elevated levels due to their fish consumption behavior patterns [24]. Although blood analysis may be one tool for examining ongoing exposure, assuming
that symptomatic individuals will show elevated blood-Hg concentrations based on a
single sample may lead to misdiagnosis and misclassification of exposure. Even within
groups or populations that appear to have Hg body burden levels in steady-state, exposure
periods largely independent of each other suggest that individual blood-Hg levels
can vary markedly. In addition, as a single blood sample may be a relatively poor
predictor of future exposure, a blood sample obtained for example at or before conception
may not provide the best foundation from which to offer pre-pregnancy advice on ways
to reduce exposure during the neurodevelopmentally sensitive stages in fetal development
or to provide exposure estimates for use in epidemiologic studies.

If further research endeavors using, for example, a more robust sample size provide
results supporting those observed in this study, then average long term Hg exposure
values obtained from hair strands may provide an alternative to blood-Hg levels. Hair-Hg
values may provide individuals overexposed or concerned about elevated exposures along
with pregnant women or women who are considering pregnancy a better means from which
to derive subsequent diet alterations so as to minimize MeHg exposure. There are concerns
with the use of hair as a metric of exposure such as ethnic differences and impact
from various available hair treatments as well as airborne-Hg deposition, but this
type of strategy could allow entire populations with potentially elevated levels of
exposure to MeHg to be protected through screening as analyses of hair for Hg using
atomic absorption spectrometers have come with decreased costs and increased efficiency
during the last decade.

Conclusion

The blood-Hg data obtained for this group of Japanese women living in Washington State
support the finding that these individuals are among the most exposed in the US and
reflect results from hair-Hg data indicating that MeHg body burdens for this group
may be stable or changing slowly across the study period. However, individual exposures
estimated on the basis of single blood samples should be treated with caution as indicators
of long-term exposure, as reliance on such spot blood samples make the prediction
of ongoing MeHg exposure highly uncertain due to the large intra-individual variability.

Competing interests

All authors declare they have no actual or potential competing financial or non-financial
interests in this work.

Authors’ contributions

AT was a significant contributor to many facets of AMIBS and was responsible for many
others. AMIBS would not have been conducted in the exemplary manner that it was without
her. RD made substantial contributions to the acquisition of data for AMIBS and was
involved in drafting the manuscript. AS aided in drafting the manuscript and, in data
analyses and interpretation of data. JWW provided substantial contribution to the
development of the manuscript and in data interpretation. FK provided significant
effort to data analyses and the interpretation of study results. TMB contributed significantly
to study design and data interpretation. EM aided in the writing of the manuscript
and contributed to the acquisition of the data. KM performed statistical analyses
and aided in drafting the manuscript. All authors contributed substantially to the
discussion of the data and their analyses, provided editorial comments to the draft
manuscript and all approved the final manuscript.

Acknowledgements

This work is supported by the US Environmental Protection Agency, Region 10 (Clean
Water Act, 104 (b)(3): 66–463), PNW Center for Human Health and Ocean Sciences (NIH/National
Institute of Environmental Health: P50 ES012762 and National Science Foundation: OCE-0434087
and OCE-0910624) and the Washington State Department of Health. Our thanks go out
to Sachiko Oshio, CNM, PhD, ARNP for her efforts and aid in obtaining blood samples.

Disclaimer

The contents of this manuscript are solely the responsibility of the authors and do
not necessarily represent the official views or policies of the NIEHS, NIH, NSF, EPA,
the New Jersey Department of Environmental Protection, the Washington State Department
of Ecology or the Washington State Department of Health.

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